Present-day environmental justice may reflect legacies of injustice in the past. The United States has a long history of racial segregation which is still visible. During the 1930s the Home Owners’ Loan Corporation (HOLC), as part of the New Deal, rated neighborhoods based on their perceived safety for real estate investment. Their ranking system, (A (green), B (blue), C (yellow), D (red)) was then used to block access to loans for home ownership. Colloquially known as “redlining”, this practice has had widely-documented consequences not only for community wealth, but also health.1 Redlined neighborhoods have less greenery2 and are hotter than other neighborhoods.3
Check out coverage by the New York Times.
A recent study found that redlining has not only affected the environments communities are exposed to, it has also shaped our observations of biodiversity.4 Community or citizen science, whereby individuals share observations of species, is generating an enormous volume of data. Ellis-Soto and co-authors found that redlined neighborhoods remain the most undersampled areas across 195 US cities. This gap is highly concerning, because conservation decisions are made based on these data.
Check out coverage by EOS.
We will be working with data from the United States Environmental Protection Agency’s EJScreen: Environmental Justice Screening and Mapping Tool.
According to the US EPA website:
This screening tool and data may be of interest to community residents or other stakeholders as they search for environmental or demographic information. It can also support a wide range of research and policy goals. The public has used EJScreen in many different locations and in many different ways.
EPA is sharing EJScreen with the public:
- to be more transparent about how we consider environmental justice in our work,
- to assist our stakeholders in making informed decisions about pursuing environmental justice and,
- to create a common starting point between the agency and the public when looking at issues related to environmental justice.
EJScreen provides on environmental and demographic information for the US at the Census tract and block group levels. You will be working with block group data that has been downloaded from the EPA site. To understand the associated data columns, you will need to explore the Technical Documentation and column description spreadsheet available in the data folder. I also encourage you to explore the limitations and caveats of the data.
A team of researchers, led by the Digital Scholarship Lab at the University of Richmond have digitized maps and information from the HOLC as part of the Mapping Inequality project.
We will be working with maps of HOLC grade designations for Los Angeles. Information on the data can be found here.5
The Global Biodiversity Information Facility is the largest aggregator of biodiversity observations in the world. Observations typically include a location and date that a species was observed.
We will be working observations of birds from 2021 onward.
Load relevant packages.
library(tidyverse)
library(sf)
library(plotly)
library(gt)
Read in EJScreen data and filter to Los Angeles County
# read in ejscreen data with direct filepath
la_county <- st_read("~/Documents/github/eds223-assignments/assignment-2-amandaherbst/data/EJSCREEN_2023_BG_StatePct_with_AS_CNMI_GU_VI.gdb/",
quiet = TRUE) %>%
# filter ejscreen to LA county
filter(CNTY_NAME == "Los Angeles County")
Wastewater Discharge
Find and visualize which LA census block groups are above the 95th percentile of national values for wastewater discharge.
# map of LA
# color census block groups by wastewater discharge
# add centroid for block groups above 95th percentile
# filter la dataset for block groups above 95th percentile for wastewater dischare
top_percentile <- la_county %>%
filter(P_PWDIS > 95)
# make centroids for block groups above 95th percentile
top_percentile_centroids <- st_centroid(top_percentile)
# map LA county and color census blocks by waste water discharge
m1 <- ggplot() +
geom_sf(data = la_county, aes(fill = PWDIS),
lwd = 0.1,
color = 'gray') +
scale_fill_viridis_c(direction = -1) +
# add above 95th percentile centroids
geom_sf(data = top_percentile_centroids,
color = 'magenta') +
labs(x = "Longtitude",
y = "Latitude",
fill = "Waste water discharge") +
# choose x axis breaks so longitude is readable
scale_x_continuous(breaks = c(-119, -118.6, -118.2, -117.8)) +
theme_bw()
# make interactive to better see where the centroids are
ggplotly(m1)